Linear algebra refresher covering vector norms, inner products, matrix multiplication, inverse matrices, and Moore-Penrose pseudo-inverse with NumPy examples.

Using Python decorators to compose nested functions for cleaner code structure.

How to use Python's @property decorator for clean getter/setter patterns and backward compatibility.

Fundamentals of Bayesian statistics covering Bayes' theorem, conditional probability, posterior updating, and causality interpretation.

CNN fundamentals: convolution operations, kernel mechanics, multi-dimensional convolutions, and backpropagation.

Deep learning fundamentals: key components (data, model, loss, optimizer) and a brief history from AlexNet to GPT-3.